Convenient parameter estimation approaches for process control systems with time-delay via step responses

This paper focuses on the problem of the parameter estimation for the control systems with a time-delay. Many control systems can be expressed by a first-order system with a time-delay, which is used widely in process control for its good characteristic of describing the dynamical systems. In this work, an easy method for estimating the parameters of the first-order system with time-delay is presented based on the observed data from the discrete-time sampling. By picking up some datum points and having some convenient operations, the convenient parameter estimation methods, i.e., the one-point method and the two-point method are developed for the time-delay systems. The numerical simulation is carried out to test the performance of the proposed approaches.

[1]  Ling Xu,et al.  Parameter estimation and controller design for dynamic systems from the step responses based on the Newton iteration , 2015 .

[2]  Meihang Li,et al.  Auxiliary Model Based Least Squares Iterative Algorithms for Parameter Estimation of Bilinear Systems Using Interval-Varying Measurements , 2018, IEEE Access.

[3]  Ling Xu,et al.  Application of the Newton iteration algorithm to the parameter estimation for dynamical systems , 2015, J. Comput. Appl. Math..

[4]  Feng Ding,et al.  Partially Coupled Stochastic Gradient Identification Methods for Non-Uniformly Sampled Systems , 2010, IEEE Transactions on Automatic Control.

[5]  F. Ding Two-stage least squares based iterative estimation algorithm for CARARMA system modeling ☆ , 2013 .

[6]  Feng Ding,et al.  Parameter estimation algorithms for dynamical response signals based on the multi-innovation theory and the hierarchical principle , 2017, IET Signal Process..

[7]  Ting Cui,et al.  Joint Multi-innovation Recursive Extended Least Squares Parameter and State Estimation for a Class of State-space Systems , 2020 .

[8]  Feng Ding,et al.  An efficient hierarchical identification method for general dual-rate sampled-data systems , 2014, Autom..

[9]  Yan Ji,et al.  Hierarchical recursive generalized extended least squares estimation algorithms for a class of nonlinear stochastic systems with colored noise , 2019, J. Frankl. Inst..

[10]  F. Ding Coupled-least-squares identification for multivariable systems , 2013 .

[11]  Feng Ding,et al.  Hierarchical Newton and least squares iterative estimation algorithm for dynamic systems by transfer functions based on the impulse responses , 2018, Int. J. Syst. Sci..

[12]  Ling Xu The parameter estimation algorithms based on the dynamical response measurement data , 2017 .

[13]  Feng Ding,et al.  Parameter estimation with scarce measurements , 2011, Autom..

[14]  T. Hayat,et al.  Hierarchical Parameter Estimation for the Frequency Response Based on the Dynamical Window Data , 2018, International Journal of Control, Automation and Systems.

[15]  Jing Chen,et al.  Hierarchical identification for multivariate Hammerstein systems by using the modified Kalman filter , 2017 .

[16]  Feng Ding,et al.  Decomposition based fast least squares algorithm for output error systems , 2013, Signal Process..

[17]  Y. Liu,et al.  Gradient-based and least-squares-based iterative estimation algorithms for multi-input multi-output systems , 2012, J. Syst. Control. Eng..

[18]  Yuhua Chen,et al.  Indirect identification of continuous-time delay systems from step responses , 2011 .

[19]  Guo-Ping Jiang,et al.  Weighted Parameter Estimation for Hammerstein Nonlinear ARX Systems , 2020, Circuits Syst. Signal Process..

[20]  Jianqiang Pan,et al.  A filtering based multi-innovation extended stochastic gradient algorithm for multivariable control systems , 2017 .

[21]  Feng Ding,et al.  Novel data filtering based parameter identification for multiple-input multiple-output systems using the auxiliary model , 2016, Autom..

[22]  J. Chu,et al.  Gradient-based and least-squares-based iterative algorithms for Hammerstein systems using the hierarchical identification principle , 2013 .

[23]  Chunqiu Guo,et al.  The Auxiliary Model Based Hierarchical Estimation Algorithms for Bilinear Stochastic Systems with Colored Noises , 2020 .

[24]  Jie Ding,et al.  Particle filtering‐based recursive identification for controlled auto‐regressive systems with quantised output , 2019, IET Control Theory & Applications.

[25]  F. Ding,et al.  Partially‐coupled least squares based iterative parameter estimation for multi‐variable output‐error‐like autoregressive moving average systems , 2019, IET Control Theory & Applications.

[26]  Tao Liu,et al.  Heating-up control with delay-free output prediction for industrial jacketed reactors based on step response identification. , 2018, ISA transactions.

[27]  Meihang Li,et al.  The least squares based iterative algorithms for parameter estimation of a bilinear system with autoregressive noise using the data filtering technique , 2018, Signal Process..

[28]  Feng Ding,et al.  Recursive Least Squares and Multi-innovation Stochastic Gradient Parameter Estimation Methods for Signal Modeling , 2017, Circuits Syst. Signal Process..

[29]  Feng Ding,et al.  Gradient based and least-squares based iterative identification methods for OE and OEMA systems , 2010, Digit. Signal Process..

[30]  Jie Ding,et al.  Particle filtering based parameter estimation for systems with output-error type model structures , 2019, J. Frankl. Inst..

[31]  Feng Ding,et al.  Hierarchical Least Squares Identification for Linear SISO Systems With Dual-Rate Sampled-Data , 2011, IEEE Transactions on Automatic Control.